Recent technological advances have enabled detailed and expansive studies of dynamic systems. For example, gene expression profiling promises to provide insight into normal biological and pathological disease processes and as such is being intensely pursued by industry and academia alike. The hope is that knowledge obtained from gene expression patterns will predict disease outcome or suggest individualized courses of therapy. While profiling at the protein level is ultimately most desirable, monitoring gene expression at the transcript level is more readily amenable with current technology. The two technologies that have emerged as the most promising gene expression tools are hybridization-based microarrays and quantitative real-time RT-PCR analysis (QPCR). With RT-PCR, Real-Time chemistries allow for the detection of PCR amplification during the reaction. Measuring the kinetics of the reaction in the early phases of PCR provides distinct advantages over traditional PCR detection, including speed and reliability of data.
Microarrays also have the advantage that they permit the simultaneous analysis of a large number of genes. Unfortunately, microarrays are not readily amenable to extensive replicate sampling because microarray analysis is labor intensive, technically demanding and requires large quantities of hybridization nucleic acid. Additionally, data interpretation is limited by the nuances of DNA hybridization kinetics and other systemic sources of error. Thus, gene expression arrays are presently best suited for prospective gene “mining,” identification of sets of genes with putative expression changes that should be independently verified and more accurately quantitated by techniques such as QPCR.
QPCR systems provide sensitive and reproducible expression quantification from small amounts of starting material (RNA, mRNA, or cDNA), but have been limited in the number of genes that can be practically analyzed. In contrast to microarrays, QPCR is best suited to accurate quantification of the direction and magnitude of change in a narrow set of genes. QPCR-based approaches derive changes in gene expression by normalizing the expression of a gene against the expression of an appropriate housekeeping gene.
However, these and other applications have been limited by conventional analytical methods, which typically include subtraction methods in which “before” and “after” data points are compared and the changed regions are identified. These methods typically use only a single before and after image, thereby providing no statistical basis to account for image acquisition variability or other forms of image noise.
For example, gene expression studies apply relative normalization techniques that assume that the level of expression of a normalizer gene is invariant. This is not always the case. Studies have reported that the expression of several commonly employed normalizer genes varies by tissue type and changes in response to experimental manipulations. However, even though there is a lack of absolutely reliable normalization, this relative or comparative normalization is the only viable option currently available to investigators pursuing QPCR analyses. The alternative, absolute quantification against a titration of standards, is both labor intensive and impractical for scale-up. Conventional image change analytical methods and other methods are also similarly limited.
Accordingly, there is a need in the art for methods and systems that will allow for the application of QPCR to a number of genes to identify those genes that are varying in a significant manner.
Broadly speaking, there is also a need in the art to apply gene-expression analysis techniques to larger-scale physical problems.